Portrait of Samira Abbasgholizadeh-Rahimi

Samira Abbasgholizadeh-Rahimi

samira.rahimi@mila.quebec
Assistant Professor, McGill University, Department of Electrical and Computer Engineering
Research Topics
Knowledge Graphs
Medical Machine Learning
Natural Language Processing
Website
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Biography

Samira Abbasgholizadeh-Rahimi (BEng, PhD) is the Canada Research Chair in Advanced Digital Primary Health Care, an assistant professor in the Department of Family Medicine at McGill University and an associate academic member at Mila – Quebec Artificial Intelligence Institute.

Rahimi is an affiliated scientist at Lady Davis Institute for Medical Research at the Jewish General Hospital, the elected president of the Canadian Operational Research Society, and director of Artificial Intelligence in Family Medicine (AIFM).

Drawing on her interdisciplinary background, her research focuses on the development and implementation of advanced digital health technologies, such as AI-enabled decision support tools, in primary health care. Her research is dedicated to enhancing the prevention and management of chronic diseases, such as cardiovascular disease, with a particular emphasis on vulnerable populations.

Rahimi‘s work as a principal investigator has been funded by the Fonds de recherche du Québec – Santé (FRQS), the Natural Sciences and Engineering Research Council (NSERC), Roche Canada, the Brocher Foundation (Switzerland), and the Strategy for Patient-Oriented Research (SPOR) of the Canadian Institutes of Health Research (CIHR).

She is the recipient of numerous awards, including the 2022 New Investigator Primary Care Research Award of North American Primary Care Research Group (NAPCRG), which recognizes exceptional contributions by emerging investigators in the field of primary care research.

Current Students

Sydney Keyamo
Master's Research - McGill University
Shayan nejadshamsi
Research Intern - McGill University
Haowei Qiu
Professional Master's - McGill University
Gauri Sharma
Master's Research - McGill University
Principal supervisor :
AJung Moon
Github
Google Scholar
Chetan Shirvankar
PhD - McGill University
chetan.shirvankar@mila.quebec

Publications

Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults
Charlene H Chu
Rune Nyrup
Kathleen Leslie
Jiamin Shi
Andria Bianchi
Alexandra Lyn
Molly McNicholl
Shehroz S Khan
Samira Abbasgholizadeh-Rahimi
A. Grenier
Abstract Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, edu… (see more)cation, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.
2022-01-20
The gerontologist (published)
doi.org
Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults
Charlene H Chu
Rune Nyrup
Kathleen Leslie
Jiamin Shi
Andria Bianchi
Alexandra Lyn
Molly McNicholl
Shehroz S Khan
Samira Abbasgholizadeh-Rahimi
Amanda Grenier
Abstract Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, edu… (see more)cation, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.
2022-01-20
The Gerontologist (published)
doi.org
Artificial Intelligence in Surgical Education: Considerations for Interdisciplinary Collaborations
Elif Bilgic
Andrew Gorgy
Meredith Young
Samira Abbasgholizadeh-Rahimi
Jason M. Harley
2021-12-10
Surgical Innovation (published)
doi.org
Artificial Intelligence in Surgical Education: Considerations for Interdisciplinary Collaborations
Elif Bilgic
A. Gorgy
Meredith Young
Samira Abbasgholizadeh-Rahimi
Jason M. Harley
Arti fi cial intelligence (AI) based devices are currently being used in the delivery of surgical care in a variety of settings. 1,2 Howeve… (see more)r, AI-enabled systems can trigger a variety of opinions and emotions, which reveals the different lenses that shape views on AI. Nonethless, work within surgical education may necessitate a more balanced view; with an acknowledgment of the participation of AI-enhanced devices in the delivery of surgical care and education
2021-12-10
Surgical Innovation (published)
doi.org
Exploring the roles of artificial intelligence in surgical education: A scoping review.
Elif Bilgic
Andrew Gorgy
Alison Yang
Michelle Cwintal
Hamed Ranjbar
Kalin Kahla
Dheeksha Reddy
Kexin Li
Helin Ozturk
Eric Zimmermann
Andrea Quaiattini
Samira Abbasgholizadeh-Rahimi
Dan Poenaru
Jason M. Harley
2021-11-30
American Journal of Surgery (published)
doi.org
Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal
Samira Abbasgholizadeh-Rahimi
France Légaré
Gauri Sharma
Patrick Archambault
Hervé Tchala Vignon Zomahoun
Sam Chandavong
Nathalie Rheault
Sabrina T Wong
Lyse Langlois
Yves Couturier
Jose L Salmeron
Marie-Pierre Gagnon
Jean Légaré
Background Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted seve… (see more)ral advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC. Objective We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings. Methods We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data. Results We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%). Conclusions We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.
2021-09-01
Journal of Medical Internet Research (published)
doi.org
Barriers and facilitators to patient engagement in patient safety from patients and healthcare professionals' perspectives: A systematic review and meta-synthesis.
Zahra Chegini
Morteza Arab‐Zozani
Sheikh Mohammed Shariful Islam
Georgia Tobiano
Samira Abbasgholizadeh-Rahimi
AIMS To explore patients' and healthcare professionals' (HCPs) perceived barriers and facilitators to patient engagement in patient safety. … (see more) METHODS We conducted a systematic review and meta-synthesis from five computerized databases, including PubMed/MEDLINE, Embase, Web of Science, Scopus and PsycINFO, as well as grey literature and reference lists of included studies. Data were last searched in December 2019 with no limitation on the year of publication. Qualitative and Mix-methods studies that explored HCPs' and patients' perceptions of barriers and facilitators to patient engagement in patient safety were included. Two authors independently screened the titles and the abstracts of studies. Next, the full texts of the screened studies were reviewed by two authors. Potential discrepancies were resolved by consensus with a third author. The Mixed Methods Appraisal Tool was used for quality appraisal. Thematic analysis was used to synthesize results. RESULTS Nineteen studies out of 2616 were included in this systematic review. Themes related to barriers included: patient unwillingness, HCPs' unwillingness, and inadequate infrastructures. Themes related to facilitators were: encouraging patients, sharing information with patients, establishing trustful relationship, establishing patient-centred care and improving organizational resources. CONCLUSION Patients have an active role in improving their safety. Strategies are required to address barriers that hinder or prevent patient engagement and create capacity and facilitate action.
2021-08-02
Nursing Forum (published)
doi.org
Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative
Charlene Esteban Ronquillo
Laura‐Maria Peltonen
Lisiane Pruinelli
Charlene H Chu
Suzanne Bakken
Ana Beduschi
Kenrick Cato
Nicholas Hardiker
Alain Junger
Martin Michalowski
Rune Nyrup
Samira Abbasgholizadeh-Rahimi
Donald Nigel Reed
Tapio Salakoski
Sanna Salanterä
Nancy Walton
Patrick Weber
Thomas Wiegand
Maxim Topaz
2021-05-18
Journal of Advanced Nursing (published)
doi.org
Continuing professional education of Iranian healthcare professionals in shared decision-making: lessons learned
Samira Abbasgholizadeh-Rahimi
Charo Rodriguez
Jordie Croteau
Alireza Sadeghpour
Amir-Mohammad Navali
France Légaré
2021-03-12
BMC Health Services Research (published)
doi.org
Shared Decision Making in Surgery: A Meta-Analysis of Existing Literature
Kacper Niburski
Elena Guadagno
Samira Abbasgholizadeh-Rahimi
Dan Poenaru
2020-09-03
The Patient - Patient-Centered Outcomes Research (published)
doi.org
User-Centered Design for Promoting Patient Engagement in Chronic Diseases Management: The Development of CONCERTO+
Marie-Pierre Gagnon
Mame Ndiaye
Alain Larouche
Guylaine Chabot
Christian Chabot
Ronald Buyl
Jean-Paul Fortin
Anik Giguère
Annie LeBlanc
France Légaré
Aude Motulsky
Claude Sicotte
Holly O Witteman
Eric Kavanagh
Frédéric Lépinay
Jacynthe Roberge
Hina Hakim
Myriam Brunet-Gauthier
Carole Délétroz
Samira Abbasgholizadeh-Rahimi … (see 2 more)
Jack Tchuente
Maxime Sasseville
Multimorbidity increases care needs among people with chronic diseases. In order to support communication between patients, their informal c… (see more)aregivers and their healthcare teams, we developed CONCERTO+, a patient portal for chronic disease management in primary care. A user-centered design comprising 3 iterations with patients and informal caregivers was performed. Clinicians were also invited to provide feedback on the feasibility of the solution. Several improvements were brought to CONCERTO+, and it is now ready to be implemented in real-life setting.
2020-06-16
Medical Informatics Europe (published)
doi.org
Prioritization of patients access to outpatient augmentative and alternative communication services in Quebec: a decision tool
Samira Abbasgholizadeh-Rahimi
Julien Dery
Marie‐eve Lamontagne
Ali Jamshidi
Emilie Lacroix
Angel B. Ruiz
Daoud Ait-Kadi
F. Routhier
Abstract Purpose A large number of people living with a chronic disability wait a long time to access publicly funded rehabilitation service… (see more)s such as Augmentative and Alternative Communication (AAC) services, and there is no standardized tool to prioritize these patients. We aimed to develop a prioritization tool to improve the organization and access to the care for this population. Methods In this sequential mixed methods study, we began with a qualitative phase in which we conducted semi-structured interviews with 14 stakeholders including patients, their caregivers, and AAC service providers in Quebec City, Canada to gather their ideas about prioritization criteria. Then, during a half-day consensus group meeting with stakeholders, using a consensus-seeking technique (i.e. Technique for Research of Information by Animation of a Group of Experts), we reached consensus on the most important prioritization criteria. These criteria informed the quantitative phase in which used an electronic questionnaire to collect stakeholders’ views regarding the relative weights for each of the selected criteria. We analyzed these data using a hybrid quantitative method called group based fuzzy analytical hierarchy process, to obtain the importance weights of the selected eight criteria. Results Analyses of the interviews revealed 48 criteria. Collectively, the stakeholders reached consensus on eight criteria, and through the electronic questionnaire they defined the selected criteria’s importance weights. The selected eight prioritization criteria and their importance weights are: person’s safety (weight: 0.274), risks development potential (weight: 0.144), psychological well-being (weight: 0.140), physical well-being (weight: 0.124), life prognosis (weight: 0.106), possible impact on social environment (weight: 0.085), interpersonal relationships (weight: 0.073), and responsibilities and social role (weight: 0.054). Conclusion In this study, we co-developed a prioritization decision tool with the key stakeholders for prioritization of patients who are referred to AAC services in rehabilitation settings. IMPLICATIONS FOR REHABILIATION Studies in Canada have shown that people in Canada with a need for rehabilitation services are not receiving publicly available services in a timely manner. There is no standardized tool for the prioritization of AAC patients. In this mixed methods study, we co-developed a prioritization tool with key stakeholders for prioritization of patients who are referred to AAC services in a rehabilitation center in Quebec, Canada.
2020-06-05
Disability and Rehabilitation: Assistive Technology (published)
doi.org